The Effects of Collateral Meridian Therapy for Knee Osteoarthritis Pain Management: A Pilot Study
Bibliographic record
Abstract
OBJECTIVE: The purpose of this preliminary study was to examine whether collateral meridian (CM) therapy was feasible in treating knee osteoarthritis (OA) pain. METHODS: Twenty-eight patients with knee OA and knee pain were randomly allocated to 2 groups. The CM group patients received CM therapy, whereas the control patients received placebo treatment for knee pain relief. Patients in the CM group received 2 CM treatments weekly for 3 weeks. The outcome measures were pain intensity on a visual analog scale, and knee function was determined using the Western Ontario and McMaster Universities Osteoarthritis Index. RESULTS: In the CM group, the posttreatment visual analog scale and Western Ontario and McMaster Universities Osteoarthritis Index scores were lower than those of the control group; a significant reduction in pain intensity (P = .02, P = .01, respectively) and improvement in knee function (P = .04, P = .03, respectively) were shown in the CM group at the second and third week. CONCLUSION: Collateral meridian therapy may be feasible and effective for knee OA pain relief and knee function recovery. Therefore, additional randomized control trials are warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".